Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals are used in wearable applications ranging from authentication to health and wellness. Although ECG and PPG signals can be measured on demand, continuous sensing of ECG/PPG signals in a Body Sensor Network (BSN) is a challenge. Continuous ambulatory monitoring of bio-signals is important to wearable applications ranging from healthcare and sports to biometric authentication. For example, continuous real-time ECG monitoring enables the monitoring of cardiac activity for extended periods (e.g., days), helps identify and mitigate potential risks, and improves wellness of the patient. Sports monitoring systems have begun to rely on continuous tracking of physiological signals for real-time performance enhancement.
However, real-time monitoring of bio-electric signals for long durations under free living conditions (e.g., on a wireless sensor node coupled to a running person) introduces stringent constraints such as enhanced battery life, efficient memory use on the wireless sensor node, noise-resilience, and adaptation to non-stationary properties of the continuously sampled signal. For example, continuous sampling of ECG signals at a rate of 500 bytes/s results in several megabytes of data in a span of a few hours.